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SUMMARY:Evaluating Baseline and Forecasting Success: Making REDD+ More Cre
 dible - E-Ping Rau\, University of Cambridge
DTSTART:20251010T120000Z
DTEND:20251010T130000Z
UID:TALK234424@talks.cam.ac.uk
CONTACT:114742
DESCRIPTION:*Abstract*\n\nProjects that aim to Reduce Emissions from tropi
 cal Deforestation and Degradation (REDD+) have great potential to mitigate
  climate change and biodiversity loss\, but substantial funding is needed 
 to scale up efforts. The trade of carbon credits\, quantified as the amoun
 t of avoided carbon emissions relative to a baseline ("counterfactual")\, 
 can be a key finance mechanism but face numerous challenges. We address th
 e challenges of 1) evaluating the method used to estimate counterfactuals 
 and 2) producing reliable forecasts of carbon outcomes in prospective proj
 ects with two studies\, using remotely sensed forest loss data and pixel m
 atching to track deforestation and carbon loss trends.\n\nIn the first stu
 dy\, we evaluated counterfactual-estimating methods with placebo "projects
 "\, where there are no REDD+ activities and where we project and counterfa
 ctual outcomes are expected to follow the same trend. We found that the ex
 -post method (estimates made after project start) outperformed the ex-ante
  methods (forecasts made at project start)\, supporting the use of ex-post
  methods for credit issuance and showcases the potential of using the plac
 ebo approach to help develop more credible counterfactual-estimating metho
 ds.\n\nIn the second study\, we used historical carbon loss to generate fo
 recasts of counterfactual carbon loss after project start\, and constructe
 d predictive models for within-project carbon loss and carbon credit produ
 ction (difference between project and counterfactual carbon loss)\, using 
 factors theorised to influence REDD+ project effectiveness (slope\, remote
 ness\, project size\, GDP\, corruption index) as predictors. Predictions f
 or both counterfactual carbon loss (goodness-of-fit: 0.62) and within-proj
 ect carbon loss (goodness-of-fit: 0.87) performed reasonably well\, but th
 e predictive performance of carbon credit production were low (goodness-of
 -fit: 0.32)\, suggesting a mismatch between the prediction of project vs. 
 counterfactual carbon losses or unknown biases that require further resear
 ch.\n\n*Bio*\n\nE-Ping is a third-year postdoc\, working with Keshav and P
 rofessor David Coomes (Plant Sciences) on using satellite data to quantify
  the benefit of emissions reduction and its permanence in tropical forest 
 conservation projects in the Reducing Emissions from Deforestation and For
 est Degradation (REDD+) framework\, with the aim of improving credibility 
 of conservation finance mechanisms through carbon markets.
LOCATION:Room GS15 at the William Gates Building and on Zoom: https://cl-c
 am-ac-uk.zoom.us/j/4361570789?pwd=Nkl2T3ZLaTZwRm05bzRTOUUxY3Q4QT09&amp\;fr
 om=addon 
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